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Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning

Greiff, Marcus LU and Berntorp, Karl LU (2020) 2020 American Control Conference p.4435-4441
Abstract
Accurate carrier-phase integer ambiguity resolu-tion is fundamental for high precision global navigation satellitesystems (GNSSs). In this paper we extend a recently proposedmixture Kalman filter solution to integer ambiguity resolution.We utilize the Fisher information matrix to project the acquiredmeasurements into a lower-dimensional subspace, formulatingan optimization program to find the projected measurementthat minimally degrades filter performance with respect to themean squared error (MSE) of the estimate. Using the projectedmeasurements, our method achieves a significant computationalspeedup while retaining the performance of the original filter.Theoretical results are presented regarding the optimal... (More)
Accurate carrier-phase integer ambiguity resolu-tion is fundamental for high precision global navigation satellitesystems (GNSSs). In this paper we extend a recently proposedmixture Kalman filter solution to integer ambiguity resolution.We utilize the Fisher information matrix to project the acquiredmeasurements into a lower-dimensional subspace, formulatingan optimization program to find the projected measurementthat minimally degrades filter performance with respect to themean squared error (MSE) of the estimate. Using the projectedmeasurements, our method achieves a significant computationalspeedup while retaining the performance of the original filter.Theoretical results are presented regarding the optimal projec-tion computation, and the claims are subsequently illustratedby simulation examples in a Monte Carlo study. (Less)
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author
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type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning
pages
6 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2020 American Control Conference
conference location
Denver, CO, United States
conference dates
2020-07-01 - 2020-07-03
external identifiers
  • scopus:85089594800
DOI
10.23919/ACC45564.2020.9147675
language
English
LU publication?
yes
id
9e60f4ad-c18f-4a62-b5d8-551dad04fd59
date added to LUP
2020-08-18 15:58:57
date last changed
2020-09-13 07:21:25
@inproceedings{9e60f4ad-c18f-4a62-b5d8-551dad04fd59,
  abstract     = {Accurate  carrier-phase  integer  ambiguity  resolu-tion is fundamental for high precision global navigation satellitesystems (GNSSs). In this paper we extend a recently proposedmixture Kalman filter solution to integer ambiguity resolution.We utilize the Fisher information matrix to project the acquiredmeasurements  into  a  lower-dimensional  subspace,  formulatingan  optimization  program  to  find  the  projected  measurementthat minimally degrades filter performance with respect to themean squared error (MSE) of the estimate. Using the projectedmeasurements, our method achieves a significant computationalspeedup  while  retaining  the  performance  of  the  original  filter.Theoretical results are presented regarding the optimal projec-tion  computation,  and  the  claims  are  subsequently  illustratedby  simulation  examples  in  a  Monte  Carlo  study.},
  author       = {Greiff, Marcus and Berntorp, Karl},
  booktitle    = {Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning},
  language     = {eng},
  month        = {07},
  pages        = {4435--4441},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Optimal Measurement Projections with Adaptive Mixture Kalman Filtering for GNSS Positioning},
  url          = {http://dx.doi.org/10.23919/ACC45564.2020.9147675},
  doi          = {10.23919/ACC45564.2020.9147675},
  year         = {2020},
}